Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis

ObjectiveSignatures of illness in vital signs of Neonatal Intensive Care Unit (NICU) patients can inform on future adverse events and outcomes. We implemented highly comparative time-series analysis to discover features and predictive analytics tools for all-cause mortality in the next 7 days, using the ubiquitous HR and SpO2 vital sign data from bedside monitors. DesignWe populated a Time Series Commons with the complete HR and SpO2 data from all infants in the University of Virginia NICU from 2009 to 2019. We calculated the results of applying over 80 members of 11 mathematical families to random ten-minute segments of 0.5Hz data each day for each infant, with varying parameter sets, resulting in 4998 algorithmic operations on each infant. We used an unsupervised mutual information-based method to cluster the results, and we selected a single representative operation from each cluster. We used our FAIRSCAPE framework to compute a detailed provenance of all computations, and we constructed a complete software library with links to the analyzed data for reproducibility and reuse. We made multivariable logistic regression models using the lasso to assay the usefulness of the algorithms. SettingNeonatal ICU Patients5957 NICU infants, of whom 206 died. Measurements and main results3555 algorithmic operations returned usable results. Twenty representative operations, selected from each of 20 unsupervised clusters, held more than 81% of the information predicting death. A multivariable model had an AUC of 0.81 for predicting death in the next 7 days. In addition, five algorithms outperformed others: moving threshold, successive increases, surprise, and a random walk model. ConclusionsHighly comparative time-series analysis revealed new vital sign metrics to identify NICU patients at the highest risk of death in the next week. This approach can facilitate the discovery of signatures of impending, potentially actionable, clinical decompensation in monitored patients.